Reduced on-line speech gesture integration during multimodal language processing in adults with moderate-severe traumatic brain injury: Evidence from eye-tracking.

Background: Language is multimodal and situated in rich visual contexts. Language is also incremental, unfolding moment-to-moment in real time, yet few studies have examined how spoken language interacts with gesture and visual context during multimodal language processing. Gesture is a rich communication cue that is integrally related to speech and often depicts concrete referents from the visual world.

Improved Scoring of the Center for Epidemiologic Studies Depression Scale - Revised: An Item Response Theory Analysis.

Unlabelled: The Center for Epidemiologic Studies Depression Scale - Revised (CESD-R) is a popular self-report screening measure for depression. A 20-item questionnaire with scores ranging from 0 to 4 for each item, the CESD-R can produce total scores ranging from 0 to 80. However, the typical scoring protocol for the CESD-R restricts the range of possible scores to between 0 and 60 to retain the same range and clinical cutoff scores as the original CES-D.

Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models.

Using Auxiliary Item Information in the Item Parameter Estimation of a Graded Response Model for a Small to Medium Sample Size: Empirical Versus Hierarchical Bayes Estimation.

Marginal maximum likelihood estimation (MMLE) is commonly used for item response theory item parameter estimation. However, sufficiently large sample sizes are not always possible when studying rare populations. In this paper, empirical Bayes and hierarchical Bayes are presented as alternatives to MMLE in small sample sizes, using auxiliary item information to estimate the item parameters of a graded response model with higher accuracy.

Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model.

Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT COV. In detecting TRT COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly.

Development and Validation of a Brief Version of the Vanderbilt Fatigue Scale for Adults: The VFS-A-10.

Objectives: Listening-related fatigue can be a significant problem for adults who struggle to hear and understand, particularly adults with hearing loss. However, valid, sensitive, and clinically useful measures for listening-related fatigue do not currently exist. The purpose of this study was to develop and validate a brief clinical tool for measuring listening-related fatigue in adults.

Incorporating Functional Response Time Effects into a Signal Detection Theory Model.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data.

Development and Evaluation of Pediatric Versions of the Vanderbilt Fatigue Scale for Children With Hearing Loss.

Purpose: Growing evidence suggests that fatigue associated with listening difficulties is particularly problematic for children with hearing loss (CHL). However, sensitive, reliable, and valid measures of listening-related fatigue do not exist. To address this gap, this article describes the development, psychometric evaluation, and preliminary validation of a suite of scales designed to assess listening-related fatigue in CHL: the pediatric versions of the Vanderbilt Fatigue Scale (VFS-Peds).

Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.

Eye-tracking has emerged as a popular method for empirical studies of cognitive processes across multiple substantive research areas. Eye-tracking systems are capable of automatically generating fixation-location data over time at high temporal resolution. Often, the researcher obtains a binary measure of whether or not, at each point in time, the participant is fixating on a critical interest area or object in the real world or in a computerized display.

Modelling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model.

A cluster randomized controlled trial (C-RCT) is common in educational intervention studies. Multilevel modelling (MLM) is a dominant analytic method to evaluate treatment effects in a C-RCT. In most MLM applications intended to detect an interaction effect, a single interaction effect (called a conflated effect) is considered instead of level-specific interaction effects in a multilevel design (called unconflated multilevel interaction effects), and the linear interaction effect is modelled.

Level-specific residuals and diagnostic measures, plots, and tests for random effects selection in multilevel and mixed models.

Multilevel data structures are often found in multiple substantive research areas, and multilevel models (MLMs) have been widely used to allow for such multilevel data structures. One important step when applying MLM is the selection of an optimal set of random effects to account for variability and heteroscedasticity in multilevel data. Literature reviews on current practices in applying MLM showed that diagnostic plots are only rarely used for model selection and for model checking.

Novel and familiar object recognition rely on the same ability.

There is recent evidence for a domain-general object recognition ability, called O, which is distinct from general intelligence and other cognitive and personality constructs. We extend the study of O by characterizing how it generalizes to the ability to recognize familiar objects and to the ability to make judgments of the average identity of ensembles of objects. We applied latent variable modeling to data collected from a sample of adults (N = 284) in three different tasks and for six different object domains (three novel and three familiar).

Not all DIF is shaped similarly.

In response to the target article by Teresi et al. (2021), we explain why the article is useful and we also present a different approach. An alternative category of differential item functioning (DIF) is presented with a corresponding way of modeling DIF, based on random person and random item effects and explanatory covariates.

Development and validation of the Vanderbilt Fatigue Scale for Adults (VFS-A).

Listening-related fatigue can be a significant burden for adults with hearing loss (AHL), and potentially those with other health or language-related issues (e.g., multiple sclerosis, traumatic brain injury, second language learners) who must allocate substantial cognitive resources to the process of listening.

Modeling Multivariate Count Time Series Data with a Vector Poisson Log-Normal Additive Model: Applications to Testing Treatment Effects in Single-Case Designs.

In education and psychology, single-case designs (SCDs) have been used to detect treatment effects using time series data in the presence or absence of intervention. One popular design variant of SCDs is a multiple-baseline design for multiple outcomes, which often collects outcomes with some form of a count. A Poisson model is a natural choice for the count outcome.

The limited role of hippocampal declarative memory in transient semantic activation during online language processing.

Recent findings point to a role for hippocampus in the moment-by-moment processing of language, including the use and generation of semantic features in certain contexts. What role the hippocampus might play in the processing of semantic relations in spoken language comprehension, however, is unknown. Here we test patients with bilateral hippocampal damage and dense amnesia in order to examine the necessity of hippocampus for lexico-semantic mapping processes in spoken language understanding.

A Markov Mixed-Effect Multinomial Logistic Regression Model for Nominal Repeated Measures with an Application to Syntactic Self-Priming Effects.

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items.

Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model.

This paper presents a dynamic tree-based item response (IRTree) model as a novel extension of the autoregressive generalized linear mixed effect model (dynamic GLMM). We illustrate the unique utility of the dynamic IRTree model in its capability of modeling differentiated processes indicated by intensive polytomous time-series eye-tracking data. The dynamic IRTree was inspired by but is distinct from the dynamic GLMM which was previously presented by Cho, Brown-Schmidt, and Lee (Psychometrika 83(3):751-771, 2018).

Self-Priming in Production: Evidence for a Hybrid Model of Syntactic Priming.

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error-driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation-based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation-based accounts predict that speakers should be able to prime themselves.

Use of Information Criteria in the Study of Group Differences in Trace Lines.

A brief review of various information criteria is presented for the detection of differential item functioning (DIF) under item response theory (IRT). An illustration of using information criteria for model selection as well as results with simulated data are presented and contrasted with the IRT likelihood ratio (LR) DIF detection method. Use of information criteria for general IRT model selection is discussed.

Ignoring a Multilevel Structure in Mixture Item Response Models: Impact on Parameter Recovery and Model Selection.

The current study investigated the consequences of ignoring a multilevel structure for a mixture item response model to show when a multilevel mixture item response model is needed. Study 1 focused on examining the consequence of ignoring dependency for within-level latent classes. Simulation conditions that may affect model selection and parameter recovery in the context of a multilevel data structure were manipulated: class-specific ICC, cluster size, and number of clusters.

A Note on in Bayesian Information Criterion for Item Response Models.

This brief report derives the in the penalty term of the Schwarz's (1978) Bayesian information criterion (BIC) for two-parameter logistic item response models. The results in this study show that the is the number of persons for fixed item models, whereas it is the number of observations (the Number of Persons times the Number of Items) for random item models. Given these results, the authors recommend researchers to calculate the BIC or to validate the BIC value that shows in the output of software instead of accepting the output value without a further check of implicit assumptions made for the software.